• phone icon +44 7459 302492 email message icon support@uplatz.com
  • Register

BUY THIS COURSE (GBP 12 GBP 29)
4.8 (2 reviews)
( 10 Students )
 

SLAM Navigation

Master SLAM algorithms and navigation pipelines to build autonomous robots, drones, and vehicles that can map unknown environments and navigate intell
( add to cart )
ALL COURSES Course URL
Save 59% Offer ends on 31-Dec-2026
Course Duration: 10 Hours
  Price Match Guarantee   Full Lifetime Access     Access on any Device   Technical Support    Secure Checkout   Course Completion Certificate
Bestseller
Trending
Popular
Coming soon (2026)

Students also bought -

Completed the course? Request here for Certificate. ALL COURSES

Autonomous navigation is one of the most transformative capabilities in modern robotics and intelligent systems. From self-driving cars and warehouse robots to drones, service robots, and augmented reality devices, machines must be able to understand where they are, map their surroundings, and move safely through dynamic environments. At the core of this capability lies SLAM — Simultaneous Localization and Mapping.
 
SLAM is the computational problem of building a map of an unknown environment while simultaneously estimating the position of an agent within that map. Unlike traditional navigation systems that rely on pre-built maps or external infrastructure such as GPS, SLAM enables true autonomy. A robot equipped with SLAM can enter an unfamiliar space, sense its surroundings, construct a map in real time, and continuously update its own position as it moves.
 
The SLAM Navigation course by Uplatz provides a comprehensive and practical understanding of how modern SLAM systems work and how they are implemented in real-world applications. This course bridges theory and practice, covering probabilistic estimation, sensor fusion, mapping algorithms, and navigation pipelines used in robotics, autonomous vehicles, drones, and AR/VR systems. Learners will gain both conceptual clarity and hands-on insight into building robust SLAM-based navigation systems.
🔍 What Is SLAM Navigation?
 
SLAM Navigation refers to the combined process of:

  • Localization – determining the position and orientation of a robot or agent

  • Mapping – building a representation of the surrounding environment

  • Navigation – planning and executing motion safely and efficiently

In SLAM, these tasks are tightly coupled. Errors in localization affect the map, and errors in the map affect localization. SLAM algorithms continuously correct both using sensor data, motion models, and probabilistic estimation.
 
Key characteristics of SLAM systems include:

  • Operation in unknown or partially known environments

  • Real-time performance

  • Robustness to sensor noise and uncertainty

  • Adaptability to dynamic environments

  • Sensor fusion across multiple data sources

SLAM is foundational to autonomy because it allows machines to perceive, reason, and act without external guidance.
⚙️ How SLAM Navigation Works
 
SLAM systems combine sensing, estimation, and control in a continuous loop.
 
1. Sensors and Perception
 
SLAM relies on sensors such as:

  • LiDAR

  • RGB cameras

  • Depth cameras

  • IMU (Inertial Measurement Unit)

  • Wheel encoders

  • Radar and ultrasonic sensors

Sensor data provides raw observations about the environment and robot motion.
 
2. Motion Model (Prediction)
 
The robot estimates its new position based on control inputs (wheel rotation, velocity, acceleration). This prediction introduces uncertainty due to noise and slippage.
 
3. Observation Model (Correction)
 
Sensor readings are compared with predicted observations. Differences are used to correct both the robot’s pose and the map.
 
4. Probabilistic Estimation
 
SLAM uses probabilistic methods to handle uncertainty, including:

  • Kalman Filters (EKF, UKF)

  • Particle Filters (FastSLAM)

  • Graph-based optimization (Pose graphs)

5. Mapping
 
Maps can be represented as:

  • Occupancy grids

  • Feature-based maps

  • Point clouds

  • Semantic maps

6. Navigation & Path Planning
 
Once localization and mapping are stable, navigation algorithms handle:

  • Global path planning (A*, Dijkstra)

  • Local planning and obstacle avoidance

  • Trajectory optimization

  • Dynamic replanning

This closed-loop process allows continuous navigation in real-world environments.
🏭 Where SLAM Navigation Is Used in the Industry
 
SLAM is a core technology across many industries:
 
1. Autonomous Vehicles
 
Self-driving cars use SLAM for lane-level localization, mapping, and obstacle tracking.
 
2. Robotics & Warehousing
 
Mobile robots navigate warehouses, factories, and fulfillment centers without fixed infrastructure.
 
3. Drones & UAVs
 
Drones use visual and LiDAR SLAM for indoor navigation, inspection, and exploration.
 
4. Service & Social Robots
 
Robots in hospitals, hotels, and homes rely on SLAM to move safely among people.
 
5. AR/VR & Mixed Reality
 
SLAM enables spatial mapping for headsets and mobile devices.
 
6. Agriculture & Mining
 
Autonomous tractors, harvesters, and mining vehicles depend on SLAM in GPS-denied environments.
 
7. Defense & Search-and-Rescue
 
SLAM supports navigation in hazardous or collapsed environments.
 
SLAM is essential wherever autonomy and environmental understanding are required.
🌟 Benefits of Learning SLAM Navigation
 
By mastering SLAM Navigation, learners gain:

  • Deep understanding of autonomous navigation systems

  • Strong foundation in probabilistic robotics

  • Hands-on knowledge of sensor fusion and mapping

  • Skills applicable to robotics, AI, and autonomous vehicles

  • Ability to design real-time navigation pipelines

  • High-demand expertise across multiple industries

SLAM knowledge positions learners at the intersection of robotics, AI, and control systems.
📘 What You’ll Learn in This Course
 
You will explore:

  • Fundamentals of localization, mapping, and navigation

  • Probabilistic robotics concepts

  • Kalman filters and particle filters

  • Visual SLAM, LiDAR SLAM, and sensor fusion

  • Graph-based SLAM and optimization

  • Path planning and obstacle avoidance

  • ROS-based SLAM pipelines

  • Navigation stacks and costmaps

  • Real-world SLAM use cases and challenges

  • Capstone: build a complete SLAM navigation system

🧠 How to Use This Course Effectively

  • Start with probabilistic estimation basics

  • Understand sensors and noise models

  • Implement simple localization algorithms

  • Progress to full SLAM pipelines

  • Practice navigation using simulated environments

  • Analyze failure cases and improve robustness

  • Complete the capstone navigation project

👩‍💻 Who Should Take This Course

  • Robotics Engineers

  • Autonomous Vehicle Engineers

  • AI & ML Engineers

  • Embedded Systems Developers

  • Mechatronics & Control Engineers

  • Students specializing in robotics or AI

  • Researchers in autonomous systems

Basic linear algebra and programming knowledge is recommended.
🚀 Final Takeaway
 
SLAM Navigation is the foundation of autonomous intelligence. By mastering SLAM, you gain the ability to build machines that understand their environment, localize themselves accurately, and navigate safely without external guidance. This course equips you with both the theory and practical skills needed to design robust, real-world autonomous navigation systems.

Course Objectives Back to Top

By the end of this course, learners will:

  • Understand SLAM principles and challenges

  • Implement localization and mapping algorithms

  • Use probabilistic filters for state estimation

  • Design navigation and path-planning pipelines

  • Integrate sensors for real-time SLAM

  • Build SLAM systems using ROS and simulations

  • Apply SLAM to robotics and autonomous systems

Course Syllabus Back to Top

Course Syllabus

Module 1: Introduction to SLAM & Navigation

  • Autonomy and robotics overview

  • SLAM problem formulation

Module 2: Sensors & Perception

  • LiDAR, cameras, IMU

  • Sensor noise and calibration

Module 3: Localization Techniques

  • Odometry

  • Kalman Filters

  • Particle Filters

Module 4: Mapping Techniques

  • Occupancy grid maps

  • Feature-based maps

  • Point cloud mapping

Module 5: Visual SLAM

  • Monocular SLAM

  • Stereo SLAM

  • RGB-D SLAM

Module 6: LiDAR SLAM

  • Scan matching

  • ICP

  • Graph-based LiDAR SLAM

Module 7: Graph-Based SLAM

  • Pose graphs

  • Loop closure

  • Optimization

Module 8: Navigation & Path Planning

  • Global planners

  • Local planners

  • Obstacle avoidance

Module 9: ROS Navigation Stack

  • Costmaps

  • Localization integration

  • Real-time navigation

Module 10: Capstone Project

  • Build a full SLAM navigation system

Certification Back to Top

Learners receive a Uplatz Certificate in SLAM Navigation & Autonomous Systems, validating expertise in localization, mapping, and autonomous navigation.

Career & Jobs Back to Top

This course prepares learners for roles such as:

  • Robotics Engineer

  • Autonomous Systems Engineer

  • SLAM Engineer

  • AI Robotics Engineer

  • Self-Driving Vehicle Engineer

  • UAV Navigation Engineer

  • Research Engineer (Robotics)

Interview Questions Back to Top

1. What is SLAM?

Simultaneous Localization and Mapping — building a map while estimating position.

2. Why is SLAM important?

It enables autonomous navigation in unknown environments.

3. What sensors are used in SLAM?

LiDAR, cameras, IMU, wheel encoders.

4. What is localization?

Estimating the robot’s pose within an environment.

5. What is mapping?

Building a representation of the environment.

6. What filters are used in SLAM?

Kalman filters and particle filters.

7. What is loop closure?

Detecting previously visited locations to correct drift.

8. What is graph-based SLAM?

An optimization-based approach using pose graphs.

9. What is ROS used for in SLAM?

Implementing and integrating SLAM and navigation pipelines.

10. What is the difference between SLAM and GPS navigation?

SLAM works without external infrastructure; GPS requires satellites.

Course Quiz Back to Top
Start Quiz
Back to Top



BUY THIS COURSE (GBP 12 GBP 29)